Image based specimen process control

ABSTRACT

Methods and systems for detecting anomalies in images of a specimen are provided. One system includes one or more computer subsystems configured for acquiring images generated of a specimen by an imaging subsystem. The computer subsystem(s) are also configured for determining one or more characteristics of the acquired images. In addition, the computer subsystem(s) are configured for identifying anomalies in the images based on the one or more determined characteristics without applying a defect detection algorithm to the images or the one or more characteristics of the images.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention generally relates to methods and systems for imagebased specimen process control.

2. Description of the Related Art

The following description and examples are not admitted to be prior artby virtue of their inclusion in this section.

Fabricating semiconductor devices such as logic and memory devicestypically includes processing a substrate such as a semiconductor waferusing a large number of semiconductor fabrication processes to formvarious features and multiple levels of the semiconductor devices. Forexample, lithography is a semiconductor fabrication process thatinvolves transferring a pattern from a reticle to a resist arranged on asemiconductor wafer. Additional examples of semiconductor fabricationprocesses include, but are not limited to, chemical-mechanical polishing(CMP), etch, deposition, and ion implantation. Multiple semiconductordevices may be fabricated in an arrangement on a single semiconductorwafer and then separated into individual semiconductor devices.

Inspection processes are used at various steps during a semiconductormanufacturing process to detect defects on specimens to drive higheryield in the manufacturing process and thus higher profits. Inspectionhas always been an important part of fabricating semiconductor devices.However, as the dimensions of semiconductor devices decrease, inspectionbecomes even more important to the successful manufacture of acceptablesemiconductor devices because smaller defects can cause the devices tofail.

Defect review typically involves re-detecting defects detected as suchby an inspection process and generating additional information about thedefects at a higher resolution using either a high magnification opticalsystem or a scanning electron microscope (SEM). Defect review istherefore performed at discrete locations on specimens where defectshave been detected by inspection. The higher resolution data for thedefects generated by defect review is more suitable for determiningattributes of the defects such as profile, roughness, more accurate sizeinformation, etc.

Metrology processes are also used at various steps during asemiconductor manufacturing process to monitor and control the process.Metrology processes are different than inspection processes in that,unlike inspection processes in which defects are detected on specimens,metrology processes are used to measure one or more characteristics ofthe specimens that cannot be determined using currently used inspectiontools. For example, metrology processes are used to measure one or morecharacteristics of specimens such as a dimension (e.g., line width,thickness, etc.) of features formed on the specimens during a processsuch that the performance of the process can be determined from the oneor more characteristics. In addition, if the one or more characteristicsof the specimens are unacceptable (e.g., out of a predetermined rangefor the characteristic(s)), the measurements of the one or morecharacteristics of the specimens may be used to alter one or moreparameters of the process such that additional specimens manufactured bythe process have acceptable characteristic(s).

Metrology processes are also different than defect review processes inthat, unlike defect review processes in which defects that are detectedby inspection are re-visited in defect review, metrology processes maybe performed at locations at which no defect has been detected. In otherwords, unlike defect review, the locations at which a metrology processis performed on specimens may be independent of the results of aninspection process performed on the specimens. In particular, thelocations at which a metrology process is performed may be selectedindependently of inspection results.

Currently, process control and yield analysis is performed using inlineinspection and metrology such as that described above. Defects may bedefined mainly by using one or more inspection parameters above acertain threshold. In other words, events from defect inspection must beabove a certain threshold to be reported as defects. In most cases,these reported events are being used to identify locations on wafers asdefective. For example, a gray level difference of 31 would be reportedas a defect if the threshold for gray level difference is set to 30.These defects are then used to classify and identify potential modefailures in processes or tools. Defects can then be classified and usedfor isolating problems in semiconductor manufacturing. However, thedefinition of defect thresholds is limited to values set based onoperating parameters within the inspection.

Due to the subtlety in manifestation of pattern deformation or randomdefects, some marginal events that are critical to device performancemay be hidden in the data and never be detected as defects. Subtledefects or deformation in patterns may be binned as nuisance ornon-critical defects. In addition, for advanced designs with tightergeometry and smaller process window, some critical defects can beundetected using traditional defect thresholds. Certain characteristics(which may be described as a combination of many optical attributes) inprinted images may impact device performance but it is difficult todefine a threshold to detect them as defects. Certain subtle failuremodes can only be identified by extracting multiple attributes frominspection images (optical or SEM) and performing analysis such as datamining or advanced correlation techniques.

Such definitions of defects also depend upon the response variables(such as parametric test data or functional test) and at the time ofdefect reporting, it is not practical to use image characteristics asmetrics to define defects. For example, critical defects may be definedby visual failure such as open, short, pattern deformation, size ofdefect etc. and correlation to electrical test such as parametric orfunctional tests or failure analysis. One of the key limitations in thecurrently used methods is that inspection data has to be binned (e.g.,sizing) or classified (shapes, nuisance, real, etc.) before being usedfor device characterization and yield improvement work and thereforeuses mostly supervised approaches. Inspection images (both optical andSEM images) contain a lot of information that is not being fullyutilized for device characterization and yield learning.

Some currently available systems are configured to acquire images forpost processing applications. For example, some currently used systemsare configured for acquiring image patches associated with defects inwafer inspection lot results. The image acquisition only provides imagepatches of the locations in which one or more defects are detected. Suchimage acquisition also does not have the capability to acquire arbitraryimages of interest that greatly limits the usefulness of the imagepost-processing applications. Some other currently used systems areconfigured for acquiring image patches from electron beam reviewresults. Such image acquisition only provides limited data samples dueto relatively low wafer coverage in electron beam review. Some imageacquisition may be performed using entire swaths of image data stored ina storage medium. Such image acquisition may capture whole swath imagesthat provide the maximum information for the post processingapplications. However, this method can only provide limited image datasets due to the extremely large data size (e.g., 40 TB of data per fullwafer scan at nominal inspection pixel size on some currently usedinspection tools). This practical constraint limits the usefulness ofthe post processing applications. In addition, there is no existingcomputing infrastructure to enable the image post processingapplications using the above acquired images as well as associated datafrom multiple data sources at once.

Accordingly, it would be advantageous to develop systems and methods fordetecting anomalies in images of a specimen that do not have one or moreof the disadvantages described above.

SUMMARY OF THE INVENTION

The following description of various embodiments is not to be construedin any way as limiting the subject matter of the appended claims.

One embodiment relates to a system configured to detect anomalies inimages of a specimen. The system includes an imaging subsystemconfigured for generating images of a specimen by directing energy toand detecting energy from the specimen. The imaging subsystem includesat least one energy source configured for generating the energy directedto the specimen and at least one detector configured for detecting theenergy from the specimen. The system also includes one or more computersubsystems coupled to the imaging subsystem. The computer subsystems(s)are configured for acquiring the images generated of the specimen. Thecomputer subsystem(s) are also configured for determining one or morecharacteristics of the acquired images. In addition, the computersubsystem(s) are configured for identifying anomalies in the imagesbased on the one or more determined characteristics without applying adefect detection algorithm to the images or the one or morecharacteristics of the images. The system may be further configured asdescribed herein.

Another embodiment relates to a computer-implemented method fordetecting anomalies in images of a specimen. The method includesgenerating images of a specimen by directing energy to and detectingenergy from the specimen with an imaging subsystem. The imagingsubsystem includes at least one energy source configured for generatingthe energy directed to the specimen and at least one detector configuredfor detecting the energy from the specimen. The method also includesacquiring the images generated of the specimen. In addition, the methodincludes determining one or more characteristics of the acquired images.The method further includes identifying anomalies in the images based onthe one or more determined characteristics without applying a defectdetection algorithm to the images or the one or more characteristics ofthe images. The acquiring, determining, and identifying steps areperformed by one or more computer subsystems coupled to the imagingsubsystem.

Each of the steps of the method described above may be further performedas described further herein. In addition, the embodiment of the methoddescribed above may include any other step(s) of any other method(s)described herein. Furthermore, the method described above may beperformed by any of the systems described herein.

Another embodiment relates to a non-transitory computer-readable mediumstoring program instructions executable on one or more computer systemsfor performing a computer-implemented method for detecting anomalies inimages of a specimen. The computer-implemented method includes the stepsof the method described above. The computer-readable medium may befurther configured as described herein. The steps of thecomputer-implemented method may be performed as described furtherherein. In addition, the computer-implemented method for which theprogram instructions are executable may include any other step(s) of anyother method(s) described herein.

BRIEF DESCRIPTION OF THE DRAWINGS

Further advantages of the present invention will become apparent tothose skilled in the art with the benefit of the following detaileddescription of the preferred embodiments and upon reference to theaccompanying drawings in which:

FIGS. 1 and 1 a are schematic diagrams illustrating side views ofembodiments of a system configured as described herein;

FIG. 2 is a block diagram illustrating another embodiment of a systemconfigured as described herein; and

FIG. 3 is a block diagram illustrating one embodiment of anon-transitory computer-readable medium storing program instructions forcausing one or more computer systems to perform a computer-implementedmethod described herein.

While the invention is susceptible to various modifications andalternative forms, specific embodiments thereof are shown by way ofexample in the drawings and are herein described in detail. The drawingsmay not be to scale. It should be understood, however, that the drawingsand detailed description thereto are not intended to limit the inventionto the particular form disclosed, but on the contrary, the intention isto cover all modifications, equivalents and alternatives falling withinthe spirit and scope of the present invention as defined by the appendedclaims.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The tennis “design,” “design data,” and “design information” as used.interchangeably herein generally refer to the physical design (layout)of an IC and data derived from the physical design through complexsimulation or simple geometric and Boolean operations. In addition, animage of a reticle acquired by a reticle inspection system and/orderivatives thereof can be used as a “proxy” or “proxies” for thedesign. Such a reticle image or a derivative thereof can serve as asubstitute for the design layout in any embodiments described hereinthat use a design. The design may include any other design data ordesign data proxies described in commonly owned U.S. Pat. No. 7,570,796issued on Aug. 4, 2009 to Zafar et al. and U.S. Pat. No. 7,676,077issued on Mar. 9, 2010 to Kulkarni et al., both of which areincorporated by reference as if fully set forth herein. In addition, thedesign data can be standard cell library data, integrated layout data,design data for one or more layers, derivatives of the design data, andfull or partial chip design data.

In addition, the “design,” “design data,” and “design information”described herein refers to information and data that is generated bysemiconductor device designers in a design process and is thereforeavailable for use in the embodiments described herein well in advance ofprinting of the design on any physical specimens such as reticles andwafers.

Turning now to the drawings, it is noted that the figures are not drawnto scale. In particular, the scale of some of the elements of thefigures is greatly exaggerated to emphasize characteristics of theelements. It is also noted that the figures are not drawn to the samescale. Elements shown in more than one figure that may be similarlyconfigured have been indicated using the same reference numerals. Unlessotherwise noted herein, any of the elements described and shown mayinclude any suitable commercially available elements.

In general, the embodiments described herein are configured for imagebased specimen (e.g., wafer or reticle) process control. For example,one embodiment relates to a system configured to detect anomalies inimages of a specimen.

As so-called “one-dimensional (1D) designs” are becoming more prevalentin semiconductor device designs, traditional approaches for identifyingsystematic and random defects may not provide enough sensitivity toprocess variation and impact to device performance. For example, in 1D,the definition of hot spots is somewhat different than previously usedhot spots because all patterns are fairly uniform. In contrast, intwo-dimensional (2D) designs, often times, there are substantiallyunique structures that tend to fail. Failures in 1D may not be readilydescribed by simple characteristics. As such, the embodiments describedherein may use the machine learning techniques described herein to findWhat is unique about certain locations in the design. However, use ofthe embodiments described herein is not limited to 1D designs.

For random defects, minimum defect size required to be captured isgetting smaller. For systematic defects, millions and billions ofidentical or similar patterns are being printed on specimens making itdifficult to identify unique pattern types that are considered to be hotspots. It is also difficult to define what level of deformation impactsdevice performance. Any excursion or anomalies (relatively smallvariation from typical values) must be identified from a relatively highvolume of inspection data and analyzed appropriately to monitor thehealth of a process and process tool(s). As such, inspection signal (notjust defect) will become an integral part of fab monitoring.

Hence, the embodiments described herein are configured to use inspectiondata such as optical inspection data and characteristics as part ofoverall fab data analysis to identify issues in semiconductor devicesearly to deter yield loss. Inspection tools such as those commerciallyavailable from KLA-Tencor, Milpitas, Calif. generate a lot of data inaddition to reporting defects, and inspection parameters and images areunique and proprietary in solving difficult yield improvement and devicecharacterization.

One embodiment of a system is shown in FIG. 1. The system includes animaging subsystem configured for generating images of a specimen bydirecting energy to and detecting energy from the specimen. The imagingsubsystem includes at least one energy source configured tor generatingthe energy directed to the specimen and at least one detector configuredfor detecting the energy from the specimen.

In some embodiments, the imaging subsystem is configured as an opticalbased imaging subsystem. For example, the system may include opticalbased imaging subsystem 10. In this manner, the at least one energysource may include at least one light source, and the at least onedetector may include at least one light based detector. In addition, theenergy directed to and detected from the specimen may include light. Inthe embodiment of FIG. 1, the optical based imaging subsystem isconfigured for scanning light over or directing light to a physicalversion of the specimen while detecting light from the specimen tothereby generate the images for the specimen. The optical based imagingsubsystem may also be configured to perform the scanning (or directing)and the detecting with multiple modes as described further herein.

In one embodiment, the specimen is a wafer. The wafer may include anywafer known in the art, In another embodiment, the specimen is areticle. The reticle may include any reticle known in the art.

In the embodiment of the system shown in FIG. 1, optical based imagingsubsystem 10 includes an illumination subsystem configured to directlight to specimen 14. The illumination subsystem includes at least onelight source. For example, as shown in FIG. 1, the illuminationsubsystem includes light source 16. In one embodiment, the illuminationsubsystem is configured to direct the light to the specimen at one ormore angles of incidence, which may include one or more oblique anglesand/or one or more normal angles. For example, as shown in FIG. 1, lightfrom light source 16 is directed through optical element 18 and thenlens 20 to specimen 14 at an oblique angle of incidence. The obliqueangle of incidence may include any suitable oblique angle of incidence,which may vary depending on, for instance, characteristics of thespecimen.

The optical based imaging subsystem may be configured to direct thelight to the specimen at different angles of incidence at differenttimes. For example, the optical based imaging subsystem may beconfigured to alter one or more characteristics of one or more elementsof the illumination subsystem such that the light can be directed to thespecimen at an angle of incidence that is different than that shown inFIG. 1. In one such example, the optical based imaging subsystem may beconfigured to move light source 16, optical element 18, and lens 20 suchthat the light is directed to the specimen at a different oblique angleof incidence or a normal (or near normal) angle of incidence.

In some instances, the optical based imaging subsystem may be configuredto direct light to the specimen at more than one angle of incidence atthe same time. For example, the illumination subsystem may include morethan one illumination channel, one of the illumination channels mayinclude light source 16, optical element 18, and lens 20 as shown inFIG. 1 and another of the illumination channels (not shown) may includesimilar elements, which may be configured differently or the same, ormay include at least a light source and possibly one or more othercomponents such as those described further herein. If such light isdirected to the specimen at the same time as the other light, one ormore characteristics (e.g., wavelength, polarization, etc.) of the lightdirected to the specimen at different angles of incidence may bedifferent such that light resulting from illumination of the specimen atthe different angles of incidence can be discriminated from each otherat the detector(s).

In another instance, the illumination subsystem may include only onelight source (e.g., source 16 shown in FIG. 1) and light from the lightsource may be separated into different optical paths (e.g., based onwavelength, polarization, etc.) by one or more optical elements (notshown) of the illumination subsystem. Light in each of the differentoptical paths may then be directed to the specimen. Multipleillumination channels may be configured to direct light to the specimenat the same time or at different times (e.g., when differentillumination channels are used to sequentially illuminate the specimen).In another instance, the same illumination channel may be configured todirect light to the specimen with different characteristics at differenttimes. For example, in some instances, optical element 18 may beconfigured as a spectral filter and the properties of the spectralfilter can be changed in a variety of different ways (e.g., by swappingout the spectral filter) such that different wavelengths of light can bedirected to the specimen at different times. The illumination subsystemmay have any other suitable configuration known in the art for directingthe light having different or the same characteristics to the specimenat different or the same angles of incidence sequentially orsimultaneously.

In one embodiment, light source 16 may include a broadband plasma (BBP)light source. In this manner, the light generated by the light sourceand directed to the specimen may include broadband light. However, thelight source may include any other suitable light source such as alaser. The laser may include any suitable laser known in the art and maybe configured to generate light at any suitable wavelength orwavelengths known in the art. In addition, the laser may be configuredto generate light that is monochromatic or nearly-monochromatic. In thismanner, the laser may be a narrowband laser. The light source may alsoinclude a polychromatic light source that generates light at multiplediscrete wavelengths or wavebands.

Light from optical element 18 may be focused onto specimen 14 by lens20. Although lens 20 is shown in FIG. 1 as a single refractive opticalelement, it is to be understood that, in practice, lens 20 may include anumber of refractive and/or reflective optical elements that incombination focus the light from the optical element to the specimen.The illumination subsystem shown in FIG. 1 and described herein mayinclude any other suitable optical elements (not shown). Examples ofsuch optical elements include, but are not limited to, polarizingcomponent(s), spectral filter(s), spatial filter(s), reflective opticalelement(s), apodizer(s), beam splitter(s), aperture(s), and the like,which may include any such suitable optical elements known in the art.In addition, the optical based imaging subsystem may be configured toalter one or more of the elements of the illumination subsystem based onthe type of illumination to be used for imaging.

The optical based imaging subsystem may also include a scanningsubsystem configured to cause the light to be scanned over the specimen.For example, the optical based imaging subsystem may include stage 22 onwhich specimen 14 is disposed during imaging. The scanning subsystem mayinclude any suitable mechanical and/or robotic assembly (that includesstage 22) that can be configured to move the specimen such that thelight can be scanned over the specimen. In addition, or alternatively,the optical based imaging subsystem may be configured such that one ormore optical elements of the optical based imaging subsystem performsome scanning of the light over the specimen. The light may be scannedover the specimen in any suitable fashion such as in a serpentine-likepath or in a spiral path.

The optical based imaging subsystem further includes one or moredetection channels. At least one of the one or more detection channelsincludes a detector configured to detect light from the specimen due toillumination of the specimen by the optical based imaging subsystem andto generate output responsive to the detected light. For example, theoptical based imaging subsystem shown in FIG. 1 includes two detectionchannels, one formed by collector 24, element 26, and detector 28 andanother formed by collector 30, element 32, and detector 34. As shown inFIG. 1, the two detection channels are configured to collect and detectlight at different angles of collection. In some instances, bothdetection channels are configured to detect scattered light, and thedetection channels are configured to detect light that is scattered atdifferent angles from the specimen. However, one or more of thedetection channels may be configured to detect another type of lightfrom the specimen (e.g., reflected light).

As further shown in FIG. 1, both detection channels are shown positionedin the plane of the paper and the illumination subsystem is also shownpositioned in the plane of the paper. Therefore, in this embodiment,both detection channels are positioned in (e.g., centered in) the planeof incidence. However, one or more of the detection channels may bepositioned out of the plane of incidence. For example, the detectionchannel formed by collector 30, element 32, and detector 34 may beconfigured to collect and detect light that is scattered out of theplane of incidence. Therefore, such a detection channel may be commonlyreferred to as a “side” channel, and such a side channel may be centeredin a plane that is substantially perpendicular to the plane ofincidence.

Although FIG. 1 shows an embodiment of the optical based imagingsubsystem that includes two detection channels, the optical basedimaging subsystem may include a different number of detection channels(e.g., only one detection channel or two or more detection channels). Inone such instance, the detection channel formed by collector 30, element32, and detector 34 may form one side channel as described above, andthe optical based imaging subsystem may include an additional detectionchannel (not shown) formed as another side channel that is positioned onthe opposite side of the plane of incidence. Therefore, the opticalbased imaging subsystem may include the detection channel that includescollector 24, element 26, and detector 28 and that is centered in theplane of incidence and configured to collect and detect light atscattering angle(s) that are at or close to normal to the specimensurface. This detection channel may therefore be commonly referred to asa “top” channel, and the optical based imaging subsystem may alsoinclude two or more side channels configured as described above. Assuch, the optical based imaging subsystem may include at least threechannels (i.e., one top channel and two side channels), and each of theat least three channels has its own collector, each of which isconfigured to collect light at different scattering angles than each ofthe other collectors.

As described further above, each of the detection channels included inthe optical based imaging subsystem may be configured to detectscattered light. Therefore, the optical based imaging subsystem shown inFIG. 1 may be configured for dark field (DF) imaging of specimens.However, the optical based imaging subsystem may also or alternativelyinclude detection channels) that are configured for bright field (BF)imaging of specimens. In other words, the optical based imagingsubsystem may include at least one detection channel that is configuredto detect light specularly reflected from the specimen. Therefore, theoptical based imaging subsystems described herein may be configured foronly DF, only BF, or both DF and BF imaging. Although each of thecollectors are shown in FIG. 1 as single refractive optical elements, itis to be understood that each of the collectors may include one or morerefractive optical element(s) and/or one or more reflective opticalelement(s).

The one or more detection channels may include any suitable detectorsknown in the art, For example, the detectors may includephoto-multiplier tubes (PMTs), charge coupled devices (CCDs), time delayintegration (TDI) cameras, and any other suitable detectors known in theart. The detectors may also include non-imaging detectors or imagingdetectors. In this manner, if the detectors are non-imaging detectors,each of the detectors may be configured to detect certaincharacteristics of the scattered light such as intensity but may not beconfigured to detect such characteristics as a function of positionwithin the imaging plane. As such, the output that is generated by eachof the detectors included in each of the detection channels of theoptical based imaging subsystem may be signals or data, but not imagesignals or image data. In such instances, a computer subsystem such ascomputer subsystem 36 may be configured to generate images of thespecimen from the non-imaging output of the detectors. However, in otherinstances, the detectors may be configured as imaging detectors that areconfigured to generate image signals or image data. Therefore, theoptical based imaging subsystem may be configured to generate the imagesdescribed herein in a number of ways.

It is noted that FIG. 1 is provided herein to generally illustrate aconfiguration of an optical based imaging subsystem that may be includedin the system embodiments described herein or that may generate imagesthat are used by the system embodiments described herein. Obviously, theoptical based imaging subsystem configuration described herein may bealtered to optimize the performance of the optical based imagingsubsystem as is normally performed when designing a commercial opticalbased system. In addition, the systems described herein may beimplemented using an existing optical based system (e.g., by addingfunctionality described herein to an existing system) such as the29xx/39xx and Puma 9xxx series of tools that are commercially availablefrom KLA-Tencor, Milpitas, Calif. For some such systems, the embodimentsdescribed herein may be provided as optional functionality of the system(e.g., in addition to other functionality of the system). Alternatively,the optical based imaging subsystem described herein may be designed“from scratch” to provide a completely new optical based system.

The system also includes one or more computer subsystems computersubsystem 36 and/or computer subsystem(s) 102) coupled to the imagingsubsystem. For example, computer subsystem 36 may be coupled to thedetectors of the optical based imaging subsystem in any suitable manner(e.g., via one or more transmission media, which may include “wired”and/or “wireless” transmission media) such that the computer subsystemcan receive the output generated by the detectors during scanning of thespecimen. Computer subsystem 36 and/or computer subsystem(s) 102 may beconfigured to perform a number of functions described further hereinusing the output of the detectors.

The computer subsystems shown in FIG. 1 (as well as other computersubsystems described herein) may also be referred to herein as computersystem(s). Each of the computer subsystem(s) or system(s) describedherein may take various forms, including a personal computer system,image computer, mainframe computer system, workstation, networkappliance, Internet appliance, or other device. In general, the term“computer system” may be broadly defined to encompass any device havingone or more processors, which executes instructions from a memorymedium. The computer subsystem(s) or system(s) may also include anysuitable processor known in the art such as a parallel processor. Inaddition, the computer subsystem(s) or system(s) may include a computerplatform with high speed processing and software, either as a standaloneor a networked tool.

If the system includes more than one computer subsystem, then thedifferent computer subsystems may be coupled to each other such thatimages, data, information, instructions, etc. can be sent between thecomputer subsystems as described further herein. For example, computersubsystem 36 may be coupled to computer subsystem(s) 102 as shown by thedashed line in FIG. 1 by any suitable transmission media, which mayinclude any suitable wired and/or wireless transmission media known inthe art. Two or more of such computer subsystems may also be effectivelycoupled by a shared computer-readable storage medium (not shown).

Although the imaging subsystem is described above as being an optical orlight-based imaging subsystem, the imaging subsystem may be configuredas an electron beam based imaging subsystem. For example, in oneembodiment, the images generated for the specimen includes electron beambased images. In addition, the at least one energy source may include atleast one electron beam source, and the at least one detector mayinclude at least one electron beam based detector. In this manner, theenergy directed to the specimen may include electrons, and the energydetected from the specimen may include electrons. In one such embodimentshown in FIG. 1a , the electron beam based imaging subsystem includeselectron column 122 coupled to computer subsystem 124. As also shown inFIG. 1a , the electron column includes electron beam source 126configured to generate electrons that are focused to specimen 128 by oneor more elements 130. The electron beam source may include, for example,a cathode source or emitter tip, and one or more elements 130 mayinclude, for example, a gun lens, an anode, a beam limiting aperture, agate valve, a beam current selection aperture, an objective lens, and ascanning subsystem, all of which may include any such suitable elementsknown in the art.

Electrons returned from the specimen (e.g., secondary electrons) may befocused by one or more elements 132 to detector 134. One or moreelements 132 may include, for example, a scanning subsystem, which maybe the same scanning subsystem included in element(s) 130.

The electron column may include any other suitable elements known in theart. In addition, the electron column may be further configured asdescribed in U.S. Pat. No. 8,664,594 issued Apr. 4, 2014 to Jiang etal., U.S. Pat. No. 8,692,204 issued Apr. 8, 2014 to Kojima et al., U.S.Pat. No. 8,698,093 issued Apr. 15, 2014 to Gubbens et al., and U.S. Pat.No. 8,716,662 issued May 6, 2014 to MacDonald et al., which areincorporated by reference as if fully set forth herein.

Although the electron column is shown in FIG. 1a as being configuredsuch that the electrons are directed to the specimen at an oblique angleof incidence and are scattered from the specimen at another obliqueangle, it is to be understood that the electron beam may be directed toand scattered from the specimen at any suitable angles. In addition, theelectron beam based imaging subsystem may be configured to use multiplemodes to generate electron beam based images for the specimen asdescribed further herein (e.g., with different illumination angles,collection angles, etc.). The multiple modes of the electron beam basedimaging subsystem may be different in any imaging parameters of theimaging subsystem.

Computer subsystem 124 may be coupled to detector 134 as describedabove. The detector may detect electrons returned from the surface ofthe specimen thereby forming electron beam based images for thespecimen. The electron beam based images may include any suitableelectron beam based images. Computer subsystem 124 may be configured toperform one or more functions described further herein for the specimenusing images generated by detector 134. Computer subsystem 124 may beconfigured to perform any additional step(s) described herein. A systemthat includes the electron beam based imaging subsystem shown in FIG. 1amay be further configured as described herein.

It is noted that FIG 1a is provided herein to generally illustrate aconfiguration of an electron beam based imaging subsystem that may beincluded in the embodiments described herein. As with the optical basedimaging subsystem described above, the electron beam based imagingsubsystem configuration described herein may be altered to optimize theperformance of the electron beam based imaging subsystem as is normallyperformed when designing a commercial electron beam based system. Inaddition, the systems described herein may be implemented using anexisting electron beam based imaging subsystem (e.g., by addingfunctionality described herein to an existing electron beam basedsystem) such as the eSxxx and eDR-xxxx series of tools that arecommercially available from KLA-Tencor. For some such systems, theembodiments described herein may be provided as optional functionalityof the electron beam based system (e.g., in addition to otherfunctionality of the system). Alternatively, the electron beam basedsubsystem described herein may be designed “from scratch” to provide acompletely new electron beam based system.

Although the imaging subsystem is described above as being configured asan optical based or electron beam based imaging subsystem, the imagingsubsystem may be configured as an ion beam based imaging subsystem. Sucha tool may be configured as shown in FIG. 1a except that the electronbeam source may be replaced with any suitable ion beam source known inthe art. In addition, the imaging subsystem may be any other suitableion beam based imaging subsystem such as those included in commerciallyavailable focused ion beam (FIB) systems, helium ion microscopy (HIM)systems, and secondary ion mass spectroscopy (SIMS) systems.

As noted above, the imaging subsystem is configured for scanning energy(e.g., light or electrons) over a physical version of the specimenthereby generating actual images for the physical version of thespecimen. In this manner, the imaging subsystem may be configured as an“actual” imaging subsystem, rather than a “virtual” imaging subsystem.For example, a storage medium (not shown) and computer subsystem(s) 102shown in FIG. 1 may be configured as a “virtual” system. In particular,the storage medium and the computer subsystem(s) are not part of opticalbased imaging subsystem 10 and do not have any capability for handlingthe physical version of the specimen. In other words, in systemsconfigured as virtual systems, the output of its one or more “detectors”may be output that was previously generated by one or more detectors ofan actual tool and that is stored in the virtual system, and during the“scanning,” the virtual system may replay the stored output as thoughthe specimen is being scanned. In this manner, scanning the specimenwith a virtual system may appear to be the same as though a physicalspecimen is being scanned with an actual system, while, in reality, the“scanning” involves simply replaying output for the specimen in the samemanner as the specimen may be scanned. Systems and methods configured as“virtual” inspection systems are described in commonly assigned U.S.Pat. No. 8,126,255 issued on Feb. 28, 2012 to Bhaskar et al. and U.S.Pat. No. 9,222,895 issued on Dec. 29, 2015 to Duffy et al., both ofwhich are incorporated by reference as if fully set forth herein. Theembodiments described herein may be further configured as described inthese patents. For example, the one or more computer subsystemsdescribed herein may be further configured as described in thesepatents.

The one or more virtual systems may also be configured as a centralcompute and storage (CCS) system, which may be configured to perform anyof the image analysis and other functions of the computer subsystem(s)described herein. The persistent storage mechanisms described herein canhave distributed computing and storage such as the CCS architecture, butthe embodiments described herein are not limited to that architecture.One such embodiment is shown in FIG. 2, which illustrates how inspectionimages optical and scanning electron microscope (SEM) images)) may beused to identify critical issues in semiconductor process and yieldimprovements as described further herein. In this manner, FIG. 2 showshow inspection and other images can be used in fab data processing. Inaddition, FIG. 2 shows how inspection and other images can be used inconcert with other sources in yield management.

In particular, as shown in FIG. 2, the system may include or be coupledto data source 200, which may include inspection tool(s) 202, defectreview tool(s) 204, metrology tool(s) 206, process tool(s) 208, analysisstation(s) 210, and design service 212. Each of the elements in datasource 200 may be configured to send output generated by the elements toprocessing unit 216, which may be configured as a CCS, via network 214.User interface 218 such as a laptop computer, personal computer (PC), orother computer systems described herein may also be coupled to each ofthe elements of data source 200 and/or processing unit 216 via network214. Processing unit 216 may be configured as one or more of thecomputer subsystems described herein. Each of the elements of the datasource may be configured as described further herein. Network 214 may befurther configured as described herein (e.g., as one or moretransmission media).

In one such embodiment, inspection tool(s) 202 may include optical basedand/or electron beam based inspection tool(s) configured for inspectionof specimens such as wafers and/or reticles. The inspection tool(s) maybe configured to generate signals, device context, and specimen context.The output produced by the inspection tool(s) may include images,characteristics, features, a noise map, and fault diagnostic control(FDC) or system data from the inspection tool(s) that can be fed into ananalysis engine. The areas on the specimens that the inspection tool(s)may be used for generating output include static random access memory(SRAM) and logic areas of the specimens. The layers of the specimens forwhich the inspection tool(s) may be configured to generate the outputinclude current layers, multiple focal planes, previous layers, andfuture layers. In addition, the inspection tool(s) may be configured todetect a variety of defect types such as systematic, random, andprogrammed defects.

Defect review tool(s) 204 may include electron beam or SEM reviewtool(s) that may be configured to output images and/or measurements ofdefects detected on a specimen. Metrology tool(s) 206 may include acritical dimension SEM (CDSEM). In addition, the metrology tool(s) maybe configured for measuring CD, thickness, refractive index (RI),flatness, resistivity (RS) or sheet resistance, etc. of patternedfeatures or films formed on the specimen. Process tool(s) 208 may beconfigured to provide a process tool datalog that may includeinformation about one or more processes performed on the specimen suchas temperature, flow rate, pressure, illumination, etc. Analysisstation(s) 210 may include failure analysis (FA) and/or fab electricaltesting tool(s) that may output parametric, functional test values, andthe like. Design service 212 may include any suitable source for designdata, including any of the design data described herein, such as anelectronic design automation (EDA) tool or a computer aided design (CAD)tool, which may include any such suitable commercially available toolsknown in the art.

Processing unit 216 may have the configuration of a CCS as describedabove. In addition, processing unit 216 may be configured as a virtualsystem as described above. Processing unit 216 may be configured toperform one or more functions of the one or more computer subsystemsdescribed herein using output generated by one or more of the toolsshown in FIG. 2. In addition, the processing unit may be configured toperform one or more additional functions for the specimens using outputgenerated by one or more of the tools shown in FIG. 2. For example, theprocessing unit may be configured for using output generated by one ormore of the tools shown in FIG. 2 for classification of defects detectedon the specimens, nuisance filtering of defects detected on thespecimens, sampling defects detected on the specimens for defect reviewand/or providing guidance for defect review of defects detected on thespecimens. In addition, the processing unit may be configured for usingoutput generated by one or more of the tools shown in FIG. 2 for opticalproximity correction (OPC)/pattern verification and fixing, printchecking, recipe set up, real time feedback, process tool monitoring,process monitoring, yield predicting, work in progress (WIP) adjustment,etc.

Processing unit 216 may also be configured for detecting defects andperforming other functions described herein using a variety ofprocessing methodologies and output generated by one or more of thetools shown in FIG. 2. For example, the processing unit may beconfigured for multiple mode inspection (e.g., volumetric and/ormulti-focal plane inspection), intra-die inspection, die-to-databaseinspection, and automatic defect classification (ADC) using outputgenerated by one or more of the tools shown in FIG. 2. The processingunit may also be configured to perform the functions described hereinusing a variety of algorithm technologies and output generated by one ormore of the tools shown in FIG. 2. For example, the processing unit maybe configured to perform the one or more functions described hereinusing output generated by one or more of the tools shown in FIG. 2 andmodeling and/or simulation, deep learning (e.g., convolutional neuralnetwork (CNN), autoencoder, etc.), correlation, pattern recognition,etc. Processing unit 216 may be further configured as described hereinwith respect to the one or more computer subsystems.

The processing unit may therefore be configured as a centralizedcomputing storage and analyzer that stores data from various sources.The processing unit may store “big data” and be able to analyzeinspection and other images in real time and offline to Characterizeimages according to usage. In this manner, the processing unit may storepost process data and perform post process applications. In addition,the processing unit may be configured for centralized computing thatconcurrently analyzes data from multiple sources to identify failures,to identify root causes, and to identify corrective actions. Theprocessing unit may also include a combination of hardware and/orsoftware. For example, the processing unit may be configured forcentralized computing with a cluster or combination of single and/ormulti-core central processing units (CPUs)/graphics processing units(GPUs)/field programmable gate arrays (FPGAs) to achieve the desiredcomputing, especially with a learning based model and/or real timethroughput requirements within a fab. As described further above, theprocessing unit may provide centralized computing with a network toconnect with various data sources such as an inspection tool, a processtool, and other processing units that collect data, pre-analyze data,and receive the data as feedback. The processing unit may also beconfigured for centralized computing with software architecture thatenables input data selection for analysis, algorithm selection foranalysis, and analysis throughput with selected data and algorithm(s).

The embodiments described herein can be implemented as software featuresthat are either independent of or integrated into actual systems (i.e.,systems with specimen handling capability such as inspection systems).As a standalone system, the embodiments described herein can alsoprovide data analysis using proprietary inspection image characteristicsthat can be extracted from inspection images. Such systems can beconnected to external data sources or connected to databases such asKlarity systems commercially available from KIA-Tencor that houseexternal data sources.

The CCS may also be created by building hardware infrastructure andsoftware platform(s). For example, networks may be built to connect datasources, which image post process applications may use, such asinspection tools and design data services to a CCS. The CCS hardwareinfrastructure may provide sufficient storage and computing capacity toenable “big data” analytics including deep learning using the storedimages and associated data with extendibility in both computing andstorage. The CCS software platform may host multiple image postprocessing applications simultaneously using distributed “big data”technology in batch and streaming modes invoked from an applicationprogram interface (API), interactive user interfaces, and command lineinterfaces. In addition, the CCS software platform may integrate datasources of inspection, metrology, design, and any other data to theimage post process applications within the CCS. The CCS may also beconfigured to enhance or add data collection capability in all datasources of interest. For example, the CCS may include mechanisms todefine selection of data of interest in the data source controlsoftware. In addition, the CCS may include mechanisms to collect andoutput data of interest to the CCS.

In the CCS software architecture, post process applications may includeApplications 1, . . . , Application N. The CCS software platform mayinclude proprietary frameworks and open source frameworks (e.g., Hadoop,Spark, Deep learning, etc.). The CCS hardware infrastructure may includeCPU, GPU, random access memory (RAM), storage, network, etc.

The data collection method for post processing may be performed usingexisting software in data sources such as data generation, dataacquisition, data selection, and data output. The post processing mayalso be performed using new software added to data sources includingdata selection for post process, which may be performed based on dataprovided by the existing data acquisition software and/or the existingdata selection software, and data output for post processing. The postprocessing may also he performed using the CCS described herein, whichmay include data ingestion software.

As further noted above, the imaging subsystem may be configured togenerate images of the specimen with multiple modes. In general, a“mode” can be defined by the values of parameters of the imagingsubsystem used for generating images of a specimen or the output used togenerate images of the specimen. Therefore, modes that are different maybe different in the values for at least one of the imaging parameters ofthe imaging subsystem. For example, in one embodiment of an opticalbased imaging subsystem, at least one of the multiple modes uses atleast one wavelength of light for illumination that is different from atleast one wavelength of the light for illumination used for at least oneother of the multiple modes. The modes may be different in theillumination wavelength as described further herein (e.g., by usingdifferent light sources, different spectral filters, etc.) for differentmodes. In another embodiment, at least one of the multiple modes uses anillumination channel of the imaging subsystem that is different from anillumination channel of the imaging subsystem used for at least oneother of the multiple modes. For example, as noted above, the tool mayinclude more than one illumination channel. As such, differentillumination channels may be used for different modes.

The optical and electron beam based imaging subsystems described hereinmay be configured as inspection imaging subsystems. The optical andelectron beam based imaging subsystems described herein may also oralternatively be configured as defect review imaging subsystems. Theoptical and electron beam based imaging subsystems described herein mayalso or alternatively be configured as metrology imaging subsystems. Inparticular, the embodiments of the imaging subsystems described hereinand shown in FIGS. 1 and 1 a may be modified in one or more parametersto provide different imaging capability depending on the application forwhich they will be used. In one such example, the imaging subsystemshown in FIG. 1 may be configured to have a higher resolution if it isto be used for defect review or metrology rather than for inspection. Inother words, the embodiments of the imaging subsystems shown in FIGS. 1and 1 a describe some general and various configurations for an imagingsubsystem that can be tailored in a number of manners that will beobvious to one skilled in the art to produce tools having differentimaging capabilities that are more or less suitable for differentapplications.

In another embodiment, the system may include a semiconductorfabrication tool configured to perform one or more fabrication processeson the specimen. In one such example, as shown in FIG. 1, the system mayinclude semiconductor fabrication tool 106, which may be coupled tocomputer subsystem(s) 102 and/or any other elements of the systemdescribed herein. The semiconductor fabrication tool may include anysuitable semiconductor fabrication tool and/or chamber known in the artsuch as a lithography track, an etch chamber, a chemical mechanicalpolishing (CMP) tool, a deposition chamber, a stripping or cleaningchamber, and the like. In addition, the semiconductor fabrication toolmay include one or more detectors (not shown in FIG. 1) such as thosedescribed further herein that are configured to generate output for thespecimen. Examples of suitable semiconductor fabrication tools that maybe included in the embodiments described herein are described in U.S.Pat. No. 6,891,627 to Levy et al. issued on May 10, 2005, which isincorporated by reference as if fully set forth herein. This patent alsodescribes examples of various detectors that may be included in orcoupled to the semiconductor fabrication tool and that can generateoutput as described further herein. The embodiments described herein maybe further configured as described in this patent.

The one or more computer subsystems are configured for acquiring theimages generated of the specimen. Acquiring the images may be performedusing one of the imaging subsystems described herein (e.g., by directinglight or an electron beam to the specimen and detecting light or anelectron beam from the specimen). In this manner, acquiring the imagesmay be performed using the physical specimen itself and some sort ofimaging hardware (e.g., a wafer inspection and/or defect review tool).In addition, the images may include any of the images described herein(e.g., inspection images, which may be optical or electron beam images,wafer inspection images, reticle inspection images, optical and SEMbased defect review images, simulated images, clips from a designlayout). However, acquiring the images does not necessarily includeimaging the specimen using imaging hardware. For example, another systemand/or method may generate the images and may store the generated imagesin one or more storage media such as a virtual inspection system asdescribed herein or another storage media described herein. Therefore,acquiring the images may include acquiring the images from the storagemedia in which they have been stored.

The computer subsystem(s) are also configured for determining one ormore Characteristics of the acquired images. The characteristic(s) thatare determined may include any suitable characteristics of the acquiredimages such as the images themselves and/or any characteristic(s) (orattribute(s)) that can be derived from the images such as brightness,energy, magnitude, intensity, etc. Such characteristics may bedetermined in any suitable manner using any suitable algorithm and/ormethod. While the extraction of inspection and other imagecharacteristics may not necessarily be new, unsupervised generation ofcharacteristics from inspection and other images and identification ofanomalies as described further herein is new. Furthermore, thecharacteristic(s) derived from images to generate defect definitions arenew as well.

In one embodiment, the acquired images for which the one or morecharacteristics are determined are not processed by the one or morecomputer subsystems prior to the determining step. For example, theembodiments described herein may use non-processed (or parameterized)inspection or other images for device and yield improvement efforts.Inspection or other images may include any of the images describedherein such as optical and electron beam based images. Therefore, theoutput images generated directly from inspection systems (e.g.,broadband, laser, and electron beam) and other imaging systems describedherein may be fed into an analysis engine for one or more applications.As described further herein, the applications may include correlation ofthe images or their characteristic(s) to external data such as, but notlimited to, electrical test and process tool data, formulating commoncharacteristic(s) among various pattern types independent of imagequality, identifying a source mechanism that results in certaincharacteristics in the images, and predicting device performance oryield relevance.

In this manner, the images for which characteristic(s) are determinedmay be raw images generated by the detector(s) of an imaging subsystem.However, in some instances, the images for which one or morecharacteristics are determined may include difference images. Thedifference images may be generated by subtracting a reference from atest image. The reference and the test image may be two different rawimages generated by the imaging subsystem at different locations on thespecimen. However, the reference may include any suitable referenceknown in the art. In the case of optical images, the difference imagemay be generated from two different optical images generated atdifferent locations on a specimen. In the case of electron beam images,the images may include multiple perspective images, e.g., imagesacquired from the left, right, top, etc.

The computer subsystem(s) are further configured for identifyinganomalies in the images based on the one or more determinedcharacteristics without applying a defect detection algorithm to theimages or the one or more characteristics of the images. For example,the computer subsystem(s) may use any or all characteristics ofinspection images (such as images, brightness, energy, magnitude,intensity, etc.) to identify anomalies within a die or across aspecimen. In this manner, the embodiments described herein may detectanomalies in images generated for a specimen, not necessarily anomalieson a specimen. Any anomalies detected in the images may then be used asdescribed further herein for a variety of applications. Unlike thecurrently used methods and systems for detecting defects on a specimen,therefore, detecting anomalies in the images does not include applying adefect detection algorithm or threshold(s) to the images or the one ormore characteristics of the images. For example, a predetermined defectdetection algorithm or threshold(s) is/are not applied to the images orthe characteristic(s) to detect the anomalies in the images. Inaddition, the anomalies detected in the images may or may not correspondto defects on the specimen. Identifying the anomalies in the images maybe further performed as described herein.

In contrast to the embodiments described herein, all of the imagecharacteristics may be made available to external processing in raw formso that a user can process and make them available for any defect reviewor analysis system. However, this approach leaves room for error wherecomplexity of data characteristics such as imaging condition may not befully known to users. Such an alternate method also limits itself inavailability and quality of predefined characteristics.

In one embodiment, the one or more computer subsystems are configuredfor identifying one or more problems with a device being formed on thespecimen based on the identified anomalies. For example, as describedfurther herein, the anomalies detected in the images can be used toidentify any problems with a device being formed on the specimen. Inthis manner, the results produced by the computer subsystems describedherein can be used to make device improvements for the device beingformed on the specimen (as in an in situ control loop and/or afeedforward control loop) and any other specimens (as in a feedbackcontrol loop).

In another embodiment, the one or more computer subsystems areconfigured for identifying one or more problems with a process performedon the specimen based on the identified anomalies. For example, thecomputer subsystem(s) may use local image characteristics such as zonalimage characteristics to identify potential sources of yield issues thatmay be related to process, tool, chamber, or process time. Therefore,the embodiments described herein may be capable of detecting issues witha process, process tool, or process time without necessarily detectingdefects on the specimen. The zonal image characteristics may bedetermined in any suitable manner (e.g., based on spatial informationassociated with the determined characteristic(s)). The process mayinclude any of the processes described herein. In one such example, thezone(s) on a specimen corresponding to abnormal values ofcharacteristic(s) of the images generated for the specimen may beidentified. The spatial information for those zone(s) may then beexamined to determine if there is a correlation between the zone(s) andthe parameters of a process performed on the specimen with a processtool. For example, some process tools may be known to produce particularsignatures in characteristics of a specimen when the process tools arefunctioning correctly versus incorrectly such as film thicknessvariations in particular areas on a wafer. Therefore, such knownspecimen characteristic signatures may be compared to imagecharacteristic signatures determined by the computer subsystem(s)described herein. If the spatial characteristics of any of the knownspecimen characteristic signatures appear to match or substantiallymatch (or otherwise be correlated to) the image characteristicsignatures, the performance of the process or process tool may bedetermined based on the matching known specimen characteristicsignature. Identifying the problem(s) with the process may, however, beperformed in any suitable manner known in the art.

In a further embodiment, the one or more computer subsystems areconfigured for altering a process performed on the specimen based on theanomalies to thereby improve yield of the process. For example,information about the anomalies identified in the images generated forthe specimen may be used as described further herein to determineproblems with the process and/or how the process is performing. Thatinformation can then be used to determine how to modify the process ifthe process appears to not be performing within its predeterminedspecifications. For example, if there is an abnormality in the imagecharacteristic(s) determined for a specimen, that abnormality may beused to determine how to correct the process to reduce the chances thatthe abnormality is observed on additional specimens on which the processis performed. Therefore, the process parameter(s) may be altered toimprove (i.e., increase) yield of the process.

In some embodiments, the determining is performed in real time while theimaging subsystem is generating the images of the specimen. For example,the computer subsystem(s) described herein may use inspection and otherimages to extract characteristic(s) in real time both in global andlocalized behavior within a given inspection or other image, and thecharacteristic(s) may be further analyzed to identify anomalies in thecharacteristic(s) that may indicate process or tool problems. Inaddition, determining the characteristic(s) may include dynamic (inmemory or within a learning based model described herein) determinationof characteristic(s) from inspection and other images in real time todescribe the condition of a given inspection or other image.

In another embodiment, the determining is performed after the imagingsubsystem is finished with generating the images of the specimen. Forexample, the computer subsystem(s) described herein may use inspectionand other images to extract characteristic(s) in post processing both inglobal and localized behavior within a given inspection or other image,and the characteristic(s) may be further analyzed to identify anomaliesin the characteristic(s) that may indicate process or tool problems. Inthis manner, determining the characteristic(s) of the images may includedynamic (in memory or within a learning based model described herein)determination of characteristic(s) from inspection and other imagesdescribed herein in post processing to describe the condition of a giveninspection or other image.

In an additional embodiment, the identifying is performed based on theone or more determined characteristics in combination with one or moreparameters of the imaging subsystem used for generating the images. Forexample, the computer subsystem(s) may be configured for linking imagecharacteristic(s) to inspection or other imaging conditions to furtherenhance the quality of the image characteristic(s). In addition, thecomputer subsystem(s) may be configured for using imaging conditions andinspection or other images to identify anomalies among one or more imagesamples. The one or more parameters of the imaging subsystem may belinked to the image characteristic(s) in any suitable manner.

In one embodiment, the computer subsystem(s) include one or morecomponents executed by the one or more computer subsystems, and thecomponent(s) include a learning based model. For example, as shown inFIG. 1, component(s) 100 executed by the computer subsystem(s), e.g.,computer subsystem 36 and/or computer subsystem(s) 102, include learningbased model 104. The one or more components may be executed by thecomputer subsystem(s) in any suitable manner. The learning based modelmay be further configured as described herein.

In one such embodiment, the model is configured for performing thedetermining the one or more characteristics of the acquired images andfor identifying the one or more characteristics that are determined forthe acquired images. For example, the one or more computer subsystemsmay be configured for algorithm based characteristic extraction fromimages and/or using a learning based model on specimen images to producelocal and/or global characteristic(s) from the images. Thecharacteristic(s) may not be predefined, but rather the computersubsystem(s) and/or the learning based model may be configured todetermine the one or more characteristics for defect data analysis.Identifying the one or more characteristics that are determined may beperformed as described further herein.

In one embodiment, the one or more characteristics of the acquiredimages include one or more local characteristics of the acquired images.In another embodiment, the one or more characteristics of the acquiredimages include one or more global characteristics of the acquiredimages. For example, the computer subsystem(s) may be configured forextracting characteristic(s) based on inspection or other images, orusing a learning based model on inspection or other images, to producelocal and global characteristics in the inspection or other images. Thelocal characteristics may include zonal image characteristics (e.g.,die, wafer, reticle). The local characteristics may be “local” in thatthey are not determined for an area on the specimen that spans anentirety of the area of the specimen. For example, the local imagecharacteristics may be determined for only a portion of a wafer, only aportion of a die on the wafer, etc. The global characteristics may be“global” in that they may be determined for an entirety of the area ofthe specimen for which the images were generated.

In some embodiments, the one or more computer subsystems are configuredfor stacking two or more of the acquired images, detecting defects inthe stacked two or more of the acquired images, and determining one ormore attributes of the detected defects based on the stacked two or moreof the acquired images. For example, the computer subsystem(s) may beconfigured for stacking images to identify potential defects or tolocalize defective sites and dynamically generate characteristic(s) ofdefects. In addition, for a given pattern or defect, multiple images canbe stacked to reduce noise and to identify where real failures may belocated. Multiple images can be aligned and overlaid using imageprocessing to discern noise from commonality of a given pattern. In onesuch example, the input to the computer subsystem(s) may include stackedimages, e.g., stacked by optics mode, focus, die, pattern, processsteps, tools, etc, The computer subsystem(s) may be configured toperform the stacking and analysis in the same neighborhood same opticalproximity correction (OPC)). The computer subsystem(s) may be configuredfor performing such stacking for applications such as defect detection,localizing defects, defect identification independent of die-to-diesubtraction, noise reduction, and automatic care area placement for hotspots. Detecting defects in the stacked two or more of the acquiredimages and determining one or more attributes of the detected defectsbased on the stacked two or more of the acquired images may be performedin any suitable manner. In other words, the stacked images may betreated as any other images and input to one or more algorithms ormethods for detecting defects and determining attributes of the detecteddefects.

In another embodiment, the one or more characteristics include at leastone characteristic of the acquired images linked to parameters of theimaging subsystem used for acquiring the images. For example, thecomputer subsystem(s) may be configured for linking imagecharacteristic(s) to imaging conditions to further enhance the qualityof the image characteristic(s). In one such example, characteristics ofimages of patterns may vary based on imaging conditions. Therefore, oncecharacteristics of certain patterns are identified, the quality of theimage characteristics may be enhanced by controlling imaging conditions.In this manner, the image quality can be improved by controlling imageconditions.

In one embodiment in which the computer subsystem(s) includecomponent(s) that include a learning based model, the one or morecomputer subsystems are configured for training the learning based modelwith the acquired images and one or more additional types of informationfor the specimen, and the learning based model is configured forpredicting information for defects on the specimen based on the acquiredimages and the one or more additional types of the information. In thismanner, defect definition is no longer based on a characteristic or aset of characteristics exceeding certain values just among inspectionparameters. A defect can be identified as having conditions where acharacteristic or set of characteristics exhibit non-typical signaturewhen individual values or index are deviating from expected behavior orthe output from the learning based model is beyond the “normal baseline”and therefore abnormal. For instance, higher sigma in magnitude within adie may be considered an anomaly and therefore would be furtherprocessed with other constituents such as that described further hereinto identify failure mode.

In addition, the computer subsystem(s) may be configured forcharacteristic based thresholding. For example, a defect may no longerbe an open or a short but a marginal pattern and characteristic(s) withcertain threshold can be used for disposition. Such defect detection maybe performed for applications such as process monitoring and inlineyield prediction.

The learning based model may also be configured for defect location,comparing identical patterns, and common modes of failure or variation.For example, defects can be classified based on physical appearance,elemental composition, design, etc. The term “common modes” here refersto common failure mechanisms such as process conditions or OPCtreatment. Multiple patterns may fail where the cause of failures may bethe same but classified differently. For instance, often several imagetypes may fail, but they may all be related to the way sub-resolutionassist features (SRAFs) were applied to a design. Another common modemay be a case where different failures are attributed to similarproximity of neighboring polygons or that one or more inspectionattributes are similar among different pattern types.

The learning based model may also be configured as a relationaldatabase. The learning based model may further be configured forcorrelation, characteristics extraction, pattern analysis, etc. Forexample, the learning based model may be configured as a statisticaldata analysis engine or machine learning system. The learning basedmodel may be configured for data mining or knowledge discovery indatabases (KDD) using a variety of sources such as statistics, patternrecognition, neuro-computing, machine learning, artificial intelligence(AI), and any other databases available to the learning based model. KDDrefers to the broad process of finding knowledge in data. In thismanner, a learning based model may be used in conjunction with specimenimages to identify defects or anomalies in processing. In addition, theembodiments described herein may be configured for a combination ofimage based analysis, data mining, and deep learning techniquesperformed using inspection and other images and extraction ofcharacteristic(s) from the images. The learning based model may also beconfigured to perform yield impact analysis, source isolation,prediction, and classification, each of which may be performed asdescribed further herein.

In one such embodiment, the one or more additional types of informationfor the specimen include design data for the specimen. For example, thelearning based model may be configured for identifying defects based onan external response variable such as design. In one such example, thelearning based model may be configured to use specimen images inconjunction with external response variables such as design to build alearning based model for correlation and predictions both in supervisedand unsupervised approaches. The learning based model can be built basedon, but not limited to, deep learning techniques such as CNN,autoencoder, or any deep learning architecture applicable to theapplications described herein.

In another such embodiment, the one or more additional types ofinformation for the specimen include electrical test and measurementdata for the specimen. The electrical test and measurement data mayinclude any suitable electrical data such as electrical test dataproduced by any suitable electrical testing of the specimen. Forexample, the learning based model may be configured for identifyingdefects based on an external response variable such as electrical testdata. In one such example, the learning based model may be configured touse specimen images in conjunction with external response variables suchas electrical data to build a learning based model for correlation andpredictions both in supervised and unsupervised approaches. In thismanner, the embodiments described herein may be configured as afunctional engine that utilizes the images and external data sources forstorage and data processing. In addition, the embodiments describedherein may be configured as a functional engine that enables bothsupervised and unsupervised approaches that enable identification ofcritical issues in process and in predicting the impact to deviceperformance and yield. The learning based model can be built based on,but not limited to, deep learning techniques such as CNN, autoencoder,or any deep learning architecture applicable to the applicationsdescribed herein.

In a further such embodiment, the one or more additional types ofinformation for the specimen include process tool data for the specimen.For example, the learning based model may be configured for identifyingdefects based on an external response variable such as process tooldata. In one such example, the learning based model may be configured touse specimen images in conjunction with external response variables suchas process tool data to build a learning based model for correlation andpredictions both in supervised and unsupervised approaches. The learningbased model can be built based on, but not limited to, deep learningtechniques such as CNN, autoencoder, or any deep learning architectureapplicable to the applications described herein.

The computer subsystem(s) and/or the learning based model may also beconfigured to perform one or more additional functions using theexternal response variables. For example, the computer subsystem(s)and/or the learning based model may be configured for using one or moreof the image characteristic(s) or the images themselves in combinationwith other response variables (e.g., electrical test or tool data) todefine defect or criticality to yield and device performance. Definingdefects or criticality to yield and device performance may be otherwiseperformed as described further herein.

In one such example, the input to the computer subsystem(s) may includeimages plus characteristic(s) for one or more layers and/or one or moredevices, The computer subsystem(s) may be configured for grouping and/orthreshold setting using such input. Grouping and/or threshold settingmay be performed for applications such as binning and/or grading, defectdefinition, determining severity of failure.

In another such example, the input to the computer subsystem(s) mayinclude images (optical and electron beam images), characteristic(s),and electrical and functional test data for one or more layers and/orone or more devices. The computer subsystem(s) may be configured foradvanced correlation modeling such as data mining or neural network (NN)using the input. In addition, or alternatively, the computersubsystem(s) may be configured for determining defects not solely by theinspection data using such inputs. The computer subsystem(s) may performsuch functions for defect definition, sampling for review and metrology,inspection optimization, and yield prediction.

In an additional such example, the input to the computer subsystem(s)may include images (e.g., optical and electron beam images),characteristic(s), and process tool datalog(s) for one or more layersand/or one or more devices. The computer subsystem(s) may be configuredto use such input for generating a model to define the impact ofprocessing conditions to changes in inspection image characteristics.The computer subsystem(s) may generate such a model for applicationsincluding in situ monitoring and in situ process optimization.

In another example, the input to the computer subsystem(s) may includecommon characteristics from multiple images. The computer subsystem(s))may be configured to use one or a set of characteristics with similarbehavior to group devices. Such functions may be performed torapplications such as inspection and/or metrology set up, in situ processadjustments, and yield correlation.

The embodiments described herein may or may not be configured fortraining the learning based model(s) described herein. For example,another method and/or system may be configured to generate a trainedlearning based model, which can then be accessed and used by theembodiments described herein.

In some such embodiments, the training includes supervised training. Forexample, in the case of a CNN, during the training phase, training data(e.g., test, reference, design, etc.) may be input to defect detectionand/or classification and manual defect review. The defect detection maybe performed using any suitable defect detection algorithms such asMDAT, which is used by some inspection tools that are commerciallyavailable from KLA-Tencor. Defect classification may be performed usingany suitable classification method such as aligning the defect images todesign and classifying the defects based on the design datacorresponding to the defect location. Manual defect review may beperformed by a user in any suitable manner known in the art. The CNN maythen be trained based on the results of the defect detection and/orclassification and manual review. In particular, the failure modes canbe defined by inputting sampled failed images and allowing the system toidentify failure modes, which may be used in classifying other patternsor defects. The results of the training may be a trained CNN. Duringruntime then, a test image may be input to the trained CNN and theoutput of the trained CNN may include a defect prediction, a defectclassification, etc.

In one such embodiment, the input may include images (inspection andother images) from one or more devices and/or one or more layers on aspecimen. The one or more components may include a CNN, a deep learningmodel configured for image analysis and/or characteristic extraction,and/or self-organizing maps. The one or more components and/or the oneor more computer subsystems may be configured for supervised learningand use, excursion detection, common failure mode identification, staticand adaptive sampling for defect review and metrology, failure mode andsource identification, tool and yield monitoring, and model and rulegeneration. For example, in a supervised approach, the image(s) may becompared to information in a database (which may include multiple datasources). Results of the comparison may be used for failure modeidentification.

In another example of supervised detection and classification, userlabeled reference and test images may be input to a CNN. The user maylabel the defects by looking at review tool images. The CNN may beconfigured as described herein. In the supervised detection andclassification approach, the i^(th) output neuron of the CNN may havehigh activation for defect class i. In such an approach, the designimage at a defect location may optionally be input to the CNN. Thedesign image at the defect location may also be optionally input to ageometry extractor. The geometry extractor may produce a geometryencoded image that may also be optionally input to the CNN for thesupervised detection and classification.

Applications of supervised classification include review sampling,recipe optimization (e.g., threshold optimization, micro care areageneration for design data, etc.), tool monitoring (e.g., criticallocation property wafer level map, critical location attribute derivedfrom inspection images), metrology (e.g., analysis of stacked dieattributes derived from inspection images), yield monitoring (e.g.,correlation of inspection image characteristics to yield), andcriticality rule finding (e.g., correlation of design attributes toinspection image characteristics).

In additional such embodiments, the training includes unsupervisedtraining. For example, the input to the learning based model may includeunlabeled inputs (in that the inputs are not labeled as defects or notdefects). With a mixture of defective and non-defective sites, alearning based model such as an autoencoder can self-organize data basedon their characteristic(s) and classify defects without supervision.Once the data can be classified according to self-organizingcharacteristic(s), then the data can be grouped or separatedautomatically.

In one such embodiment, the input may include images (e.g., inspectionand other images) from one or more devices and/or one or more layers ona specimen. The one or more components may include a CNN, a deeplearning model configured for image analysis and/or characteristicextraction, and/or self-organizing maps. The one or more componentsand/or the one or more computer subsystems may be configured forunsupervised learning and usage, adaptive learning, intra-dieinspection, die, reticle, and wafer level signature identification, andidentification of abnormal pattern (signature) to identify unknownexcursions. For example, in an unsupervised approach, the image(s) maybe used to generate one or more self-organized maps. The self-organizedmaps may be used to identify common failure modes and/or excursions. Theidentified common failure modes and/or excursions can be used for deviceimprovement.

In one such example of unsupervised classification, the input to anautoencoder may include an unlabeled inspection lot, which may includeinspection reference images and inspection defect images. In theunsupervised classification approach, the autoencoder may have a set ofoutput neurons. In this manner, an autoencoder can be used as a deepunsupervised learning NN. In such an approach, the design image at adefect location may optionally be input to the autoencoder. The designimage at the defect location may also be optionally input to a geometryextractor. The geometry extractor may produce a geometry encoded imagethat may also be optionally input to the autoencoder for theunsupervised detection and classification.

Applications of unsupervised classification include user-guided adaptivereview sampling (e.g., output of neurons corresponding to fuzzy clustersof input images, review images corresponding to the maximum activationof each output neuron that is presented to the user, who may dynamicallyadjust what he/she wants to sample, etc.), intra-die inspection withinspection images of the same pattern of interest (POI) (e.g., outlierdetection using output neuron activation values), wafer signatureidentification (e.g., outlier detection with POI inspection imagesacross a wafer), and power assisted defect sampling and labeling forsupervised classification. Outlier detection using output neuronactivation values may be performed when analyzing a pattern or a defectusing an image of the same pattern. For example, there may be certainexpected characteristics for a given pattern. When a pattern is normal,it should exhibit similar properties that are expected for the givenpattern. Once a pattern deviates from the expected properties, thesample would be considered an outlier based on the neuron activationvalues (e.g., characteristic(s) or property/properties of a givenpattern).

The learning based model may include a machine learning model. Machinelearning can be generally defined as a type of artificial intelligence(AI) that provides computers with the ability to learn without beingexplicitly programmed. Machine learning focuses on the development ofcomputer programs that can teach themselves to grow and change whenexposed to new data. In other words, machine learning can be defined asthe subfield of computer science that “gives computers the ability tolearn without being explicitly programmed.” Machine learning exploresthe study and construction of algorithms that can learn from and makepredictions on data—such algorithms overcome following strictly staticprogram instructions by making data driven predictions or decisions,through building a model from sample inputs.

The machine learning described herein may be further performed asdescribed in “Introduction to Statistical Machine Learning,” bySugiyama, Morgan Kaufmann, 2016, 534 pages; “Discriminative, Generative,and Imitative Learning,” Jebara, MIT Thesis, 2002, 212 pages; and“Principles of Data Mining (Adaptive Computation and Machine Learning),”Hand et al., MIT Press, 2001, 578 pages; which are incorporated byreference as if fully set forth herein. The embodiments described hereinmay be further configured as described in these references.

In another such embodiment, the learning based model includes a deeplearning based model, Generally speaking, “deep learning” (also known asdeep structured learning, hierarchical learning or deep machinelearning) is a branch of machine learning based on a set of algorithmsthat attempt to model high level abstractions in data. In a simple case,there may be two sets of neurons: ones that receive an input signal andones that send an output signal. When the input layer receives an input,it passes on a modified version of the input to the next layer. In adeep network, there are many layers between the input and output (andthe layers are not made of neurons but it can help to think of it thatway), allowing the algorithm to use multiple processing layers, composedof multiple linear and non-linear transformations.

Deep learning is part of a broader family of machine learning methodsbased on learning representations of data. An observation (e.g., animage) can be represented in many ways such as a vector of intensityvalues per pixel, or in a more abstract way as a set of edges, regionsof particular shape, etc. Some representations are better than others atsimplifying the learning task (e.g., face recognition or facialexpression recognition). One of the promises of deep learning isreplacing handcrafted features with efficient algorithms forunsupervised or semi-supervised feature learning and hierarchicalfeature extraction.

Research in this area attempts to make better representations and createmodels to learn these representations from large-scale unlabeled data.Some of the representations are inspired by advances in neuroscience andare loosely based on interpretation of information processing andcommunication patterns in a nervous system, such as neural coding whichattempts to define a relationship between various stimuli and associatedneuronal responses in the brain.

Various deep learning architectures such as deep neural networks,convolutional deep neural networks, deep belief networks and recurrentneural networks have been applied to fields like computer vision,automatic speech recognition, natural language processing, audiorecognition and bioinformatics where they have been shown to producestate-of-the-art results on various tasks.

In a further embodiment, the learning based model includes a neuralnetwork. For example, the model may be a deep neural network with a setof weights that model the world according to the data that it has beenfed to train it. Neural networks can be generally defined as acomputational approach which is based on a relatively large collectionof neural units loosely modeling the way a biological brain solvesproblems with relatively large clusters of biological neurons connectedby axons. Each neural unit is connected with many others, and links canbe enforcing or inhibitory in their effect on the activation state ofconnected neural units. These systems are self-learning and trainedrather than explicitly programmed and excel in areas where the solutionor feature detection is difficult to express in a traditional computerprogram.

Neural networks typically consist of multiple layers, and the signalpath traverses from front to back. The goal of the neural network is tosolve problems in the same way that the human brain would, althoughseveral neural networks are much more abstract. Modern neural networkprojects typically work with a few thousand to a few million neuralunits and millions of connections. The neural network may have anysuitable architecture and/or configuration known in the art.

In another embodiment, the learning based model includes a CNN. Forexample, the embodiments described herein can take advantage of deeplearning concepts such as a CNN to solve the normally intractablerepresentation conversion problem (e.g., rendering). The model may haveany CNN configuration or architecture known in the art.

In a further embodiment, the learning based model includes a deep neuralnetwork. For example, the model may be configured to have a deeplearning architecture in that the model may include multiple layers,which perform a number of algorithms or transformations. In general, thenumber of layers in the model is not significant and is use casedependent. For practical purposes, a suitable range of layers includedin the model is from 2 layers to a few tens of layers. The deep neuralnetwork may be otherwise configured as described herein. In one suchembodiment, the learning based model may be configured as a deep CNN(DCNN) as described in “ImageNet Classification with Deep ConvolutionalNeural Networks,” by Krizhevsky et al., NIPS, 2012, 9 pages, which isincorporated by reference as if fully set forth herein. The embodimentsdescribed herein may be further configured as described in thisreference.

In an additional embodiment, the learning based model includes adiscriminative model. Discriminative models, also called conditionalmodels, are a class of models used in machine learning for modeling thedependence of an unobserved variable y on an observed variable x. Withina probabilistic framework, this is done by modeling the conditionalprobability distribution P (y|x), which can be used for predicting yfrom x. Discriminative models, as opposed to generative models, do notallow one to generate samples from the joint distribution of x and y.However, for tasks such as classification and regression that do notrequire the joint distribution, discriminative models can yield superiorperformance. On the other hand, generative models are typically moreflexible than discriminative models in expressing dependencies incomplex learning tasks. In addition, most discriminative models areinherently supervised and cannot easily be extended to unsupervisedlearning. Application specific details ultimately dictate thesuitability of selecting a discriminative versus generative model. Thediscriminative model may be further configured as described in thereference incorporated above by Krizhevsky. In this manner, theembodiments described herein may use a deep learning network of adiscriminative type for the applications described herein(classification or regression purposes).

In some embodiments, the learning based model includes a generativemodel. A “generative” model can be generally defined as a model that isprobabilistic in nature. In other words, a “generative” model is not onethat performs forward simulation or rule-based approaches and, as such,a model of the physics of the processes involved in generating an actualimage (for which a simulated image is being generated) is not necessary.Instead, as described further herein, the generative model can belearned (in that its parameters can be learned) based on a suitabletraining set of data. The generative model may be further configured asdescribed in U.S. patent application Ser. No. 15/176,139 by Zhang et al.filed Jun. 7, 2016, which is incorporated as if fully set forth herein.The embodiments described herein may be further configured as describedin this patent application. In this manner, the embodiments describedherein may use a deep learning network such as a deep generative networkfor the applications described herein (classification or regressionpurposes).

In one embodiment, the learning based model includes a deep generativemodel. For example, the model may be configured to have a deep learningarchitecture in that the model may include multiple layers, whichperform a number of algorithms or transformations. In general, thenumber of layers on one or both sides of the generative model is notsignificant and is use case dependent. For practical purposes, asuitable range of layers on both sides is from 2 layers to a few tens oflayers.

In another such embodiment, the learning based model includes anautoencoder. An autoencoder, autoassociator or Diabolo network is anartificial neural network used for unsupervised learning of efficientcodings. The aim of an autoencoder is to learn a representation(encoding) for a set of data, typically for the purpose ofdimensionality reduction. Recently, the autoencoder concept has becomemore widely used for learning generative models of data.Architecturally, the simplest form of an autoencoder is a feedforward,non-recurrent neural network very similar to the multilayer perceptron(MLP)—having an input layer, an output layer and one or more hiddenlayers connecting them—, but with the output layer having the samenumber of nodes as the input layer, and with the purpose ofreconstructing its own inputs (instead of predicting the target valuegiven inputs). Therefore, autoencoders are unsupervised learning models.An autoencoder always consists of two parts, the encoder and thedecoder. Various techniques exist to prevent autoencoders from learningthe identity function and to improve their ability to capture importantinformation and learn richer representations. The autoencoder mayinclude any suitable type of autoencoder such as a Denoisingautoencoder, sparse autoencoder, variational autoencoder, andcontractive autoencoder.

In one embodiment, the computer subsystem(s) are configured forpredicting defect information for the specimen based on the one or moredetermined characteristics and one or more additional types ofinformation for the specimen. For example, the one or more computersubsystems may be configured to use one or more image characteristics orthe images themselves combined with other response variables (e.g.,electrical test or tool data) to define defects on the specimen. Inparticular, correlations between the external response variables and theimage characteristic(s) can be determined. The image characteristic(s)and the correlations can then be used to predict defects on the specimenwithout the external response variables and/or without applying a defectdetection algorithm to the images generated for the specimen. Inaddition, as described further herein, once anomalies in the imagecharacteristic(s) have been identified by the computer subsystem(s) asdescribed further herein, the external response variables may be used toidentify the anomalies in the image characteristic(s) that correspond todefects and the anomalies in the image characteristic(s) that do notcorrespond to defects on the specimen.

In another embodiment, the computer subsystem(s) are configured forpredicting yield criticality of defects on the specimen based on the oneor more determined characteristics and one or more additional types ofinformation for the specimen. For example, the one or more computersubsystems may be configured to use one or more image characteristics orthe images themselves combined with other response variables (e.g.,electrical test or tool data) to define defect criticality to yield. Inone such example, the image characteristic(s) and the other responsevariables can be used to determine the effect that the defects will haveon the device being formed on the specimen and therefore on the yield ofthe process being performed to fabricate the device on the specimen.

In an additional embodiment, the computer subsystem(s) are configuredfor predicting performance of devices being formed on the specimen basedon the one or more determined characteristics and one or more additionaltypes of information for the specimen. For example, the one or morecomputer subsystems may be configured to use one or more imagecharacteristics or the images themselves combined with other responsevariables (e.g., electrical test or tool data) to predict deviceperformance. In one such example, the image characteristic(s) and theother response variables can be used to determine the effect that thedefects will have on the device being formed on the specimen andtherefore on the performance of the devices being formed on thespecimen.

In some embodiments, the computer subsystem(s) are configured forcreating the one or more characteristics in real time based on one ormore additional types of information. For example, the computersubsystem(s) may be configured for real time creation of relevantcharacteristic(s) based on response variables (e.g., defect type such asopen or bridge, nuisance, etc.). In this manner, the computersubsystem(s) may not be configured to generate a predeterminedcharacteristic or predetermined set of characteristics. Instead, theembodiments described herein can be configured to analyze any and all ofthe image characteristics that can be determined from the specimenimages and can be configured to select one or more of thecharacteristics for anomaly identification and/or other steps describedherein based on the response variables.

In one embodiment, the computer subsystem(s) are configured forperforming the acquiring, determining, and identifying steps formultiple specimens, monitoring the one or more characteristics of theanomalies for changes, and altering a parameter of a process tool inresponse to the changes detected by the monitoring. For example, thecomputer subsystem(s) may be configured to use a change in pattern(behavior) of image characteristic(s) to detect problems and applysubsequent changes to processing tools. In other words, when the imagecharacteristic(s) of specimens appear to drift over time (or exhibit adramatic change from one specimen to another), the change in the imagecharacteristic(s) may be identified as corresponding to a potentialproblem in the process performed on the specimen. The change in theimage characteristic(s) can then be used to determine how to change theprocess to return the image characteristic(s) to their original values.

In another embodiment, the one or more computer subsystems areconfigured for determining one or more spatial characteristics of theanomalies and identifying a potential source of the anomalies based onthe determined one or more spatial characteristics. For example, thecomputer subsystem(s) may use local image characteristics such as zonalimage characteristics to identify potential sources of yield issues thatmay be related to process, tool, chamber, or process time. In thismanner, the input to the steps performed by the computer subsystem(s)may include spatial and time distribution of inspection images across awafer/reticle/die, etc. The spatial patterns may represent differentpotential failure modes due to process/tool variations. The spatialpatterns can then be used for process monitoring, tool monitoring, andprocess improvement based on a correlation between the spatial patternsand the process parameters, the tool parameters, and the process. Thecorrelation may be determined in any suitable manner (e.g.,experimentally or theoretically).

In an additional embodiment, the computer subsystem(s) are configuredfor stacking two or more of the acquired images and identifying one ormore weak points in a design for the specimen based on the stacked twoor more of the acquired images. For example, the computer subsystem(s)may be configured for using stacked images to identify weak points in adesign. In particular, when more than one specimen image are overlaidwith each other (i.e., stacked), any anomalies or defects that appearrepeatedly at more than one instance of the same within design locationor at more than one instance of the same patterned feature in the designindicate that that within design location or patterned feature is a weakpoint in the design (i.e., that location or patterned feature appears tobe more susceptible to defects than other locations or patternedfeatures in the design).

In a further embodiment, the computer subsystem(s) are configured forcreating an index based on the one or more characteristics of theacquired images representing potential impact of one or more attributesof the specimen, corresponding to the one or more characteristics of theimages, on performance of devices being formed on the specimen. Forexample, the computer subsystem(s) may be configured for using one ormore of the image characteristics to formulate an index for potentialimpact to device performance. In one such example, an abnormality indexmay be determined based on one or more characteristics from the images.When normalized, the potential risk of each device may be predictedusing the characteristic(s) determined from the images. In this manner,the index may be used for device evaluation and/or ranking based onseverity of abnormality.

The embodiments described herein have a number of advantages overcurrently used methods and systems. For example, using inspection andother images described herein and their characteristics as describedherein provides additional sensitivity to manufacturing issues. Usingimages and their characteristics as described herein can reduce falsepositives (irrelevant events) and missed real problems (yield relevantissues) by maximizing the information available in the images. Ratherthan relying on reported defects or a set of predefined characteristics,use of the images allows discovery of hidden problems by using advancedcorrelation engines of unsupervised data mining such as neural networksand deep learning. Furthermore, by using images, a user can takeadvantage of advancement in data processing or correlation engines wherenew characteristics can be dynamically extracted for improved modelingand applications.

Each of the embodiments of each of the systems described above may becombined together into one single embodiment.

Another embodiment relates to a computer-implemented method fordetecting anomalies in images of a specimen. The method includesgenerating images of a specimen by directing energy to and detectingenergy from the specimen with an imaging subsystem. The imagingsubsystem includes at least one energy source configured for generatingthe energy directed to the specimen and at least one detector configuredfor detecting the energy from the specimen. The method also includesacquiring the images generated of the specimen and determining one ormore characteristics of the acquired images. In addition, the methodincludes identifying anomalies in the images based on the one or moredetermined characteristics without applying a defect detection algorithmto the images or the one or more characteristics of the images. Theacquiring, determining, and identifying steps are performed by one ormore computer subsystems coupled to the imaging subsystem.

Each of the steps of the method may be performed as described furtherherein. The method may also include any other step(s) that can beperformed by the system, imaging subsystem, computer subsystem(s),component(s), and/or model(s) described. herein. The imaging subsystem,one or more computer systems, one or more components, and model may beconfigured according to any of the embodiments described herein, e.g.,imaging subsystem 10, computer subsystem(s) 102, component(s) 100, andmodel 104, respectively. In addition, the method described above may beperformed by any of the system embodiments described herein.

An additional embodiment relates to a non-transitory computer-readablemedium storing program instructions executable on one or more computersystems for performing a computer-implemented method for detectinganomalies in images of a specimen. One such embodiment is shown in FIG.3. In particular, as shown in FIG. 3, non-transitory computer-readablemedium 300 includes program instructions 302 executable on computersystem(s) 304. The computer-implemented method may include any step(s)of any method(s) described herein.

Program instructions 302 implementing methods such as those describedherein may be stored on computer-readable medium 300. Thecomputer-readable medium may be a storage medium such as a. magnetic oroptical disk, a magnetic tape, or any other suitable non-transitorycomputer-readable medium known in the art.

The program instructions may be implemented in any of various ways,including procedure-based techniques, component-based techniques, and/orobject-oriented techniques, among others. For example, the programinstructions may be implemented using ActiveX controls, C++ objects,JavaBeans, Microsoft Foundation Classes is (“MFC”), SSE (Streaming SIMDExtension) or other technologies or methodologies, as desired.

Computer system(s) 304 may be configured according to any of theembodiments described herein.

Further modifications and alternative embodiments of various aspects ofthe invention will be apparent to those skilled in the art in view ofthis description. For example, methods and systems for detectinganomalies in images of a specimen are provided. Accordingly, thisdescription is to be construed as illustrative only and is for thepurpose of teaching those skilled in the art the general manner ofcarrying out the invention. It is to be understood that the forms of theinvention shown and described herein are to be taken as the presentlypreferred embodiments. Elements and materials may be substituted forthose illustrated and described herein, parts and processes may bereversed, and certain features of the invention may be utilizedindependently, all as would be apparent to one skilled in the art afterhaving the benefit of this description of the invention. Changes may bemade in the elements described herein without departing from the spiritand scope of the invention as described in the following claims.

What is claimed is:
 1. A system configured to detect anomalies in imagesof a specimen, comprising: an imaging subsystem configured forgenerating images of a specimen by directing energy to and detectingenergy from the specimen, wherein the imaging subsystem comprises atleast one energy source configured for generating the energy directed tothe specimen and at least one detector configured for detecting theenergy from the specimen; and one or more computer subsystems coupled tothe imaging subsystem, wherein the one or more computer subsystems areconfigured for: acquiring the images generated of the specimen, whereinthe acquired images comprise optical images or electron beam images;determining one or more characteristics of the acquired images; andidentifying anomalies in the optical images or the electron beam imagesbased on the one or more determined characteristics without applying apredetermined defect detection algorithm to the images or the one ormore characteristics of the images, and wherein the one or more computersubsystems comprise one or more components executed by the one or morecomputer subsystems, and wherein the one or more components comprise alearning based model configured for performing said determining the oneor more characteristics of the acquired images and for identifying theone or more characteristics that are determined for the acquired images.2. The system of claim 1, wherein the acquired images for which the oneor more characteristics are determined are not processed by the one ormore computer subsystems prior to said determining.
 3. The system ofclaim 1, wherein the one or more computer subsystems are furtherconfigured for identifying one or more problems with a device beingformed on the specimen based on the identified anomalies.
 4. The systemof claim 1, wherein the one or more computer subsystems are furtherconfigured for identifying one or more problems with a process performedon the specimen based on the identified anomalies.
 5. The system ofclaim 1, wherein the one or more computer subsystems are furtherconfigured for altering a process performed on the specimen based on theanomalies to thereby improve yield of the process.
 6. The system ofclaim 1, wherein said determining is performed in real time while theimaging subsystem is generating the images of the specimen.
 7. Thesystem of claim 1, wherein said determining is performed after theimaging subsystem is finished generating the images of the specimen. 8.The system of claim 1, wherein said identifying is performed based onthe one or more determined characteristics in combination with one ormore parameters of the imaging subsystem used for generating the images.9. The system of claim 1, wherein the one or more characteristics of theacquired images comprise one or more local characteristics of theacquired images.
 10. The system of claim 1, wherein the one or morecharacteristics of the acquired images comprise one or more globalcharacteristics of the acquired images.
 11. The system of claim 1,wherein the one or more computer subsystems are further configured forstacking two or more of the acquired images, detecting defects in thestacked two or more of the acquired images, and determining one or moreattributes of the detected defects based on the stacked two or more ofthe acquired images.
 12. The system of claim 1, wherein the one or morecharacteristics comprise at least one characteristic of the acquiredimages linked to parameters of the imaging subsystem used for acquiringthe images.
 13. The system of claim 1, wherein the one or more computersubsystems are further configured for training the learning based modelwith the acquired images and one or more additional types of informationfor the specimen, and wherein the learning based model is furtherconfigured for predicting information for defects on the specimen basedon the acquired images and the one or more additional types of theinformation.
 14. The system of claim 13, wherein the one or moreadditional types of information for the specimen comprise design datafor the specimen.
 15. The system of claim 13, wherein the one or moreadditional types of information for the specimen comprise electricaltest and measurement, data for the specimen.
 16. The system of claim 13,wherein the one or more additional types of information for the specimencomprise process tool data for the specimen.
 17. The system of claim 13,wherein the training comprises supervised training.
 18. The system ofclaim 13, wherein the training comprises unsupervised training.
 19. Thesystem of claim 13, wherein the learning based model comprises a deeplearning based model.
 20. The system of claim 13, wherein the learningbased model comprises a convolutional neural network.
 21. The system ofclaim 13, wherein the learning based model comprises an autoencoder. 22.The system of claim 1, wherein the one or more computer subsystems arefurther configured for predicting defect information for the specimenbased on the one or more determined characteristics and one or moreadditional types of information for the specimen.
 23. The system ofclaim 1, wherein the one or more computer subsystems are furtherconfigured for predicting yield criticality of defects on the specimenbased on the one or more determined characteristics and one or moreadditional types of information for the specimen.
 24. The system ofclaim 1, wherein the one or more computer subsystems are furtherconfigured for predicting performance of devices being formed on thespecimen based on the one or more determined characteristics and one ormore additional types of information for the specimen.
 25. The system ofclaim 1, wherein the one or more computer subsystems are furtherconfigured for creating the one or more characteristics in real timebased on one or more additional types of information.
 26. The system ofclaim 1, wherein the one or more computer subsystems are furtherconfigured for performing said acquiring, determining, and identifyingfor multiple specimens, monitoring the one or more characteristics orthe anomalies for changes, and altering a parameter of a process tool inresponse to the changes detected by the monitoring.
 27. The system ofclaim 1, wherein the one or more computer subsystems are furtherconfigured for determining one or more spatial characteristics of theanomalies and identifying a potential source of the anomalies based onthe determined one or more spatial characteristics.
 28. The system ofclaim 1, wherein the one or more computer subsystems are furtherconfigured for stacking two or more of the acquired images andidentifying one or more weak points in a design for the specimen basedon the stacked two or more of the acquired images.
 29. The system ofclaim 1, wherein the one or more computer subsystems are furtherconfigured for creating an index based on the one or morecharacteristics of the acquired images representing potential impact ofone or more attributes of the specimen, corresponding to the one or morecharacteristics of the images, on performance of devices being formed onthe specimen.
 30. The system of claim 1, wherein the imaging subsystemis further configured as an optical based imaging subsystem.
 31. Thesystem of claim 1, wherein the imaging subsystem is further configuredas an electron beam based imaging subsystem.
 32. The system of claim 1,wherein the specimen is a wafer.
 33. The system of claim 1, wherein thespecimen is a reticle.
 34. A non-transitory computer-readable medium,storing program instructions executable on one or more computer systemsfor performing a computer-implemented method for detecting anomalies inimages of a specimen, wherein the computer-implemented method comprises:generating images of a specimen by directing energy to and detectingenergy from the specimen with an imaging subsystem, wherein the imagingsubsystem comprises at least one energy source configured for generatingthe energy directed to the specimen and at least one detector configuredfor detecting the energy from the specimen; acquiring the imagesgenerated of the specimen, wherein the acquired images comprise opticalimages or electron beam images; determining one or more characteristicsof the acquired images; and identifying anomalies in the optical imagesor the electron beam images based on the one or more determinedcharacteristics without applying a predetermined defect detectionalgorithm to the images or the one or more characteristics of theimages, wherein said acquiring, said determining, and said identifyingare performed by one or more computer subsystems coupled to the imagingsubsystem, wherein the one or more computer subsystems comprise one ormore components executed by the one or more computer subsystems, andwherein the one or more components comprise a learning based modelconfigured for performing said determining the one or morecharacteristics of the acquired images and for identifying the one ormore characteristics that are determined for the acquired images.
 35. Acomputer-implemented method for detecting anomalies in images of aspecimen, comprising generating images of a specimen by directing energyto and detecting energy from the specimen with an imaging subsystem,wherein the imaging subsystem comprises at least one energy sourceconfigured for generating the energy directed to the specimen and atleast one detector configured for detecting the energy from thespecimen; acquiring the images generated of the specimen, wherein theacquired images comprise optical images or electron beam images;determining one or more characteristics of the acquired images; andidentifying anomalies in the optical images or the electron beam imagesbased on the one or more determined characteristics without applying apredetermined defect detection algorithm to the images or the one ormore characteristics of the images, wherein said acquiring, saiddetermining, and said identifying are performed by one or more computersubsystems coupled to the imaging subsystem, wherein the one or morecomputer subsystems comprise one or more components executed by the oneor more computer subsystems, and wherein the one or more componentscomprise a learning based model configured for performing saiddetermining the one or more characteristics of the acquired images andfor identifying the one or more characteristics that are determined forthe acquired images.